The sharing economy has experienced massive growth in the short-term shared-home rental industry. However, few studies have investigated the determinants of the number of customer reviews received by these shared-homes. To fill this gap, we were motivated to propose an analytical framework that identified these determinants, both explicit and implicit. We applied Poisson, Quasi-Poisson, and Negative Binomial regressions with a dataset consisting of Airbnb properties from ten different cities worldwide, while successful bookings were proxied by the count of customer reviews posted by guests. We performed a cluster analysis based on the properties to generate homogeneous “cluster cities” and performed the regressions separately for each cluster. Among host-generated features, superhost, host duration, bedrooms, and amenities became significant. Among user-generated features, overall review scores and negative sentiments were significant. We also found that the “superhost” badge moderated the effects of host-generated content on the count of customer reviews. Consequently, guests paid a higher “price per night” for “superhost” properties, while they overlooked crucial attributes such as “website features.” Through these novel “cluster-specific” recommendations, our study extends the existing theories and contributes to the literature of decision analytics and tourism management. Finally, we performed a sensitivity analysis to check for the timeliness and robustness of these determinants.

共享经济在短期共享住房租赁行业中经历了巨大的增长。但是，很少有研究调查这些共享房屋收到的​​顾客评论数量的决定因素。为了填补这一空白，我们有动机提出了一个分析框架，该框架可以确定这些决定因素，包括显性的和隐性的。我们将Poisson，Quasi-Poisson和Negative Binomial回归应用于包含来自全球十个不同城市的Airbnb属性的数据集，而成功的预订则取决于客人发表的客户评论数量。我们基于属性进行聚类分析以生成同质的“聚类城市”，并对每个聚类分别进行回归。在主机生成的功能中，超级主机，主机时间，卧室和便利设施变得很重要。在用户生成的功能中，总体评论评分和负面情绪非常重要。我们还发现，“超级主机”徽章减轻了主机生成的内容对客户评论数量的影响。因此，来宾为“超级主机”属性支付了更高的“每晚价格”，而忽略了诸如“网站功能”之类的关键属性。通过这些新颖的“特定于集群的”建议，我们的研究扩展了现有理论，并为决策分析和旅游管理的文献做出了贡献。最后，我们进行了敏感性分析，以检查这些决定因素的及时性和稳健性。